Machine Learning Classification Algorithms to Predict aGvHD following Allo-HSCT: A Systematic Review

Cirruse Salehnasab; Abbas Hajifathali; Farkhondeh Asadi; Elham Roshandel; Alireza Kazemi; Arash Roshanpoor

doi:10.1055/s-0040-1709150

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2019; 58(06): 205-212
DOI: 10.1055/s-0040-1709150

Review Article

Georg Thieme Verlag KG Stuttgart · New York

Machine Learning Classification Algorithms to Predict aGvHD following Allo-HSCT: A Systematic Review

Cirruse Salehnasab

¹Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

,

Abbas Hajifathali

²Hematopoietic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

,

Farkhondeh Asadi

¹Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

,

Elham Roshandel

²Hematopoietic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

,

Alireza Kazemi

¹Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

,

Arash Roshanpoor

³Department of Computer Science, Sama Technical and Vocational Training College, Tehran Branch (Tehran), Islamic Azad University (IAU), Tehran, Iran

› Author Affiliations

Abstract

Background The acute graft-versus-host disease (aGvHD) is the most important cause of mortality in patients receiving allogeneic hematopoietic stem cell transplantation. Given that it occurs at the stage of severe tissue damage, its diagnosis is late. With the advancement of machine learning (ML), promising real-time models to predict aGvHD have emerged.

Objective This article aims to synthesize the literature on ML classification algorithms for predicting aGvHD, highlighting algorithms and important predictor variables used.

Methods A systemic review of ML classification algorithms used to predict aGvHD was performed using a search of the PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases undertaken up to April 2019 based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statements. The studies with a focus on using the ML classification algorithms in the process of predicting of aGvHD were considered.

Results After applying the inclusion and exclusion criteria, 14 studies were selected for evaluation. The results of the current analysis showed that the algorithms used were Artificial Neural Network (79%), Support Vector Machine (50%), Naive Bayes (43%), k-Nearest Neighbors (29%), Regression (29%), and Decision Trees (14%), respectively. Also, many predictor variables have been used in these studies so that we have divided them into more abstract categories, including biomarkers, demographics, infections, clinical, genes, transplants, drugs, and other variables.

Conclusion Each of these ML algorithms has a particular characteristic and different proposed predictors. Therefore, it seems these ML algorithms have a high potential for predicting aGvHD if the process of modeling is performed correctly.

Keywords

machine learning - prediction - classification - Allo-HSCT - aGvHD

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References

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Supplementary Material